With the rapid advancement of synthetic aperture radar (SAR) sensors, it has become more important to extract change information between high-resolution SAR images. Considering the efficacy and robustness of segmentation-based strategy in solving fine-resolution images, as well as the excellent performance of deep learning in feature extraction, therefore, in this paper, we propose an unsupervised region-level graph convolutional network (URGCN) for SAR change detection. Firstly, we propose a multi-temporal joint segmentation method to generate multi-level regions. This method can simultaneously segment bi-temporal SAR images and automatically align bi-temporal regions during segmentation process. We then perform pre-classification on difference images generated by neighborhood ratio detector. The high confidence training samples are selected through hierarchical clustering. Thirdly, we encode the segmented regions are as a graph structure and construct GCN model. The graph can capture the spatial and temporal structure information. Finally, the GCN model parameters are optimized by labeled samples and inferring unlabeled regions. Experimental results on two VHR SAR change detection datasets demonstrated that the proposed method can extract complete change information with high accuracy compared to alternative approaches.
Digital Elevation Models (DEMs) depict the configuration of the Earth surface, which is essential for remote sensing image ortho-rectification. Nowadays, the ALOS DEM, ASTER GDEM and SRTM DEM are the most commonly used models, produced based on different remote sensing datasets. Geolocation errors may be introduced with different producing methods and datasets. Therefore, multi-sources remote sensing images covering various terrain in China are experimented for the analysis of ortho-rectification accuracy using different DEMs. Based on the rational function model, the geolocation error of original imagery can be reduced greatly with the grounds control points, and different DEMs are used for the ortho-rectification of different imagery. The accuracy is experimented and verified using high-precision reference images. Experimental results show that ortho-rectificated images with the ALOS DEM can achieve better accuracy compared with the SRTM DEM and ASTER GDEM, which is significant for geometric processing of remote sensing images.
The change detection in built-up areas within very high resolution synthetic aperture radar images is a very challenging task due to speckle noise and geometric distortions caused by the unique imaging mechanism. To tackle this issue, we propose an object-based coarse-to-fine change detection method that integrates segmentation and uncertainty analysis techniques. First, we propose a multi-temporal joint multi-scale segmentation method for generating multi-scale segmentation masks with hierarchical nested relationships. Second, we use the neighborhood ratio detector and Jensen–Shannon distance to produce both pixel-level and object-level change maps, respectively. These maps are fused using the Demeter–Shafer evidence theory, resulting in an initial change map. We then apply a threshold to classify parcels within the initial change map into three categories: changed, unchanged, and uncertain. Third, we perform uncertainty analysis and implement progressive classification by support vector machine for uncertain parcels, moving from coarse to fine segmentation levels. Finally, we integrate change maps across all scales to obtain the final change map. The proposed method is evaluated on three datasets from the GF-3 and ICEYE-X6 satellites. The results show that our approach outperforms alternative methods in extracting more comprehensive changed regions.
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